Microsoft AI-901 - Microsoft Azure AI Fundamentals (Updated Version)

The Model catalog is used for comparing and deploying a wide range of models for generative AI development in Microsoft Foundry.

Microsoft documentation states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications . It also includes many models across providers such as Azure OpenAI, Mistral, Meta, Cohere, NVIDIA, and Hugging Face.

The Microsoft Learn module for Foundry models also states that the model catalog is used to explore and filter models , compare models using benchmark metrics , and deploy a model to an endpoint .

The requirement is to extract structured fields, including nested values, from invoices by using a defined schema . In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.

Microsoft’s Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft’s quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.

Why the other options are incorrect:

A. transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction.

B. OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema.

D. Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.

Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools .

Microsoft Foundry Agent Service documentation states that instructions define goals, constraints, and behavior for an agent. Therefore, instructions are used to guide how the generative AI model or agent should respond and behave.

Option A is correct because instructions can define constraints the model must follow.

Option B is correct because instructions can define the agent’s role and behavior.

Options C, D, and E are incorrect because Azure region, model selection, and TPM allocation are configuration or deployment/resource settings, not purposes of prompt instructions.

When using the Azure OpenAI Responses API with a vision-enabled model, the image must be included as an input image content item . The correct content item type is:

{"type": "input_image", "image_url": image_url}

Microsoft’s Azure OpenAI Responses API documentation states that the Responses API supports image inputs, and multimodal requests use structured input content items for the request.

Why the other options are incorrect:

A. image_base64 = Incorrect. Base64 data can be used as the image data format, but the content item type is still input_image.

B. image_generation = Incorrect. This is related to generating images, not providing an image for analysis.

C. output_image = Incorrect. This would refer to generated output, not image input.

D. input_image = Correct.

The completed sentence is:

Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final output is an example of the Microsoft responsible AI principle of accountability .

Accountability means people and organizations remain responsible for AI systems and their effects. Human review and responsibility for final decisions are examples of accountability.

The other options are incorrect:

fairness focuses on avoiding bias and treating people fairly.

privacy and security focuses on protecting data and restricting access.

transparency focuses on explaining AI use, capabilities, and limitations.